331239d40c0942886649f21ed46fa9ecd8d8255b,deepchem/models/tf_new_models/multitask_regressor.py,MultitaskGraphRegressor,__init__,#MultitaskGraphRegressor#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,31

Before Change

    self.final_loss = final_loss
    self.model = model
    self.sess = tf.Session(graph=self.model.graph)
    if logdir is not None:
      if not os.path.exists(logdir):
        os.makedirs(logdir)
    else:
      logdir = tempfile.mkdtemp()
    self.logdir = logdir

    with self.model.graph.as_default():
      // Extract model info 
      self.batch_size = batch_size
      self.pad_batches = pad_batches
      // Get graph topology for x
      self.graph_topology = self.model.get_graph_topology()
      self.feat_dim = n_feat

      // Building outputs
      self.outputs = self.build()
      self.loss_op = self.add_training_loss(self.final_loss, self.outputs)

      self.learning_rate = learning_rate
      self.T = learning_rate_decay_time
      self.optimizer_type = optimizer_type

      self.optimizer_beta1 = beta1
      self.optimizer_beta2 = beta2

      // Set epsilon
      self.epsilon = 1e-7
      self.add_optimizer()

      // Initialize
      self.init_fn = tf.global_variables_initializer()
      self.sess.run(self.init_fn)

      // Path to save checkpoint files, which matches the
      // replicated supervisor"s default path.
      self._save_path = os.path.join(logdir, "model.ckpt")

  def build(self):
    // Create target inputs
    self.label_placeholder = tf.placeholder(

After Change

               pad_batches=True,
               verbose=True):

    super().__init__(self, model_dir=logdir, verbose=verbose)
    self.n_tasks = n_tasks
    self.final_loss = final_loss
    self.model = model
    self.sess = tf.Session(graph=self.model.graph)

Instances